Perception

Affordance

An affordance is an action possibility that an object or environment offers an agent, a concept introduced by psychologist James J. Gibson: a handle affords pulling, a flat surface affords placing. In robot learning, affordance prediction typically means estimating dense, pixel- or point-level maps of where and how an object can be grasped, pushed, or articulated, providing an action-centric intermediate representation between raw perception and motor commands.

Why it matters for physical AI

Affordance representations transfer across object instances and embodiments better than raw trajectories, giving foundation models a compact interface for grounding language commands like "open the drawer" into actionable regions.